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# NVIDIA NeMo Evaluator

The goal of NVIDIA NeMo Evaluator is to advance and refine state-of-the-art methodologies for model evaluation, and deliver them as modular evaluation packages (evaluation containers and pip wheels) that teams can use as standardized building blocks.

# Quick start guide

NVIDIA NeMo Evaluator provide you with evaluation clients, that are specifically built to evaluate model endpoints using our Standard API.

## Launching an evaluation for an LLM

1. Install nemo-evaluator
    ```
    pip install nemo-evaluator
    ```

2. Install the package
    ```
    pip install nvidia-genai-perf-eval --index-url https://gitlab-master.nvidia.com/api/v4/projects/153108/packages/pypi/simple    
    ```

3. (Optional) Set a token to your API endpoint if it's protected
    ```bash
    export MY_API_KEY="your_api_key_here"
    ```

4. List the available evaluations:
    ```bash
    $ nemo-evaluator ls
      Available tasks:
      * genai_perf_generation (in genai_perf_eval)
      * genai_perf_summarization (in genai_perf_eval)
    ...

    ```

5. Run the evaluation. Remember to set the tokenizer:
    ```bash
    nemo-evaluator run_eval \
       --eval_type genai_perf_generation \
       --model_id meta/llama-3.1-8b-instruct \
       --model_url https://integrate.api.nvidia.com/v1/chat/completions \
       --model_type chat \
       --api_key_name MY_API_KEY \
       --output_dir /workspace/results \
       --overrides="config.params.extra.tokenizer=meta-llama/Llama-3.1-70B-Instruct,target.api_endpoint.stream=true"
    ```

    **It is recommended to turn the streaming on if your endpoint supports it (`target.api_endpoint.stream=true`).**
    Otherwise only a subset of metrics can be calculated.

    If your tokenizer comes from a gated repofitory (like in the example above), you will need to set the HF_TOKEN to authenticate before running the evaluation:
    
    ```bash
    export HF_TOKEN=...
    ```

6. Gather the results
    ```bash
    cat /workspace/results/results.yml
    ```

# Command-Line Tool

Each package comes pre-installed with a set of command-line tools, designed to simplify the execution of evaluation tasks. Below are the available commands and their usage for the `genai_perf_eval`:

## Commands

### 1. **List Evaluation Types**

```bash
nemo-evaluator ls
```

Displays the evaluation types available within the harness.

### 2. **Run an evaluation**

The `nemo-evaluator run_eval` command executes the evaluation process. Below are the flags and their descriptions:

### Required flags
* `--eval_type <string>`
The type of evaluation to perform
* `--model_id <string>`
The name or identifier of the model to evaluate.
* `--model_url <url>`
The API endpoint where the model is accessible.
* `--model_type <string>`
The type of the model to evaluate, currently either "chat" or "completions".
* `--output_dir <directory>`
The directory to use as the working directory for the evaluation. The results, including the results.yml output file, will be saved here. Make sure to use the absolute path.
* `--overrides="config.params.extra.tokenizer=<tokenizer>"`
The name of the HuggingFace tokenizer used for evaluation (e.g. `meta-llama/Llama-3.1-70B-Instruct`). If it comes from a gated repository, you will need to export `HF_TOKEN=...` to authenticate.

### Optional flags
* `--api_key_name <string>`
The name of the environment variable that stores the Bearer token for the API, if authentication is required.
* `--run_config <path>`
Specifies the path to a  YAML file containing the evaluation definition.

### Example

```bash
export HF_TOKEN=...

nemo-evaluator run_eval \
    --eval_type genai_perf_generation \
    --model_id my_model \
    --model_type chat \
    --model_url http://localhost:8000/v1/chat/completions \
    --output_dir /workspace/evaluation_results \
    --overrides="config.params.extra.tokenizer=meta-llama/Llama-3.1-70B-Instruct,target.api_endpoint.stream=true"
```

If the model API requires authentication, set the API key in an environment variable and reference it using the `--api_key_name` flag:

```bash
export MY_API_KEY="your_api_key_here"
export HF_TOKEN=...

nemo-evaluator run_eval \
    --eval_type genai_perf_generation \
    --model_id my_model \
    --model_type chat \
    --model_url http://localhost:8000/v1/chat/completions \
    --api_key_name MY_API_KEY \
    --output_dir /workspace/evaluation_results \
    --overrides="config.params.extra.tokenizer=meta-llama/Llama-3.1-70B-Instruct,target.api_endpoint.stream=true"
```

# Configuring evaluations via YAML

Evaluations in NVIDIA NeMo Evaluator are configured using YAML files that define the parameters and settings required for the evaluation process. These configuration files follow a standard API which ensures consistency across evaluations.

Example of a YAML config:
```yaml
config:
  type: genai_perf_generation
  params:
    parallelism: 50
    limit_samples: 20
    extra:
        tokenizer: microsoft/Phi-4-mini-instruct
target:
  api_endpoint:
    model_id: microsoft/phi-4-mini-instruct
    type: chat
    url: https://integrate.api.nvidia.com/v1/chat/completions
    api_key_name: NVIDIA_API_KEY
    stream: true
```

The priority of overrides is as follows:
1. command line arguments
2. user config (as seen above)
3. task defaults (defined per task type)
4. framework defaults 

`--dry_run` option allows you to print the final run configuration and command without executing the evaluation.

### Example:

```bash
nemo-evaluator run_eval \
    --eval_type genai_perf_generation \
    --model_id my_model \
    --model_type chat \
    --model_url http://localhost:8000/v1/chat/completions \
    --output_dir /workspace/evaluation_results \
    --overrides="config.params.extra.tokenizer=meta-llama/Llama-3.1-70B-Instruct" \
    --dry_run
```

Output:

```bash
Rendered config:

command: genai_perf_eval --model_id {{target.api_endpoint.model_id}} --url {{target.api_endpoint.url}}  {%
  if target.api_endpoint.api_key_name is not none %}--api-key {{target.api_endpoint.api_key_name}}
  {% endif %} --concurrencies {{config.params.parallelism}} --isl {{config.params.extra.isl}}
  --osl {{config.params.extra.osl}}  --tokenizer {{config.params.extra.tokenizer}}
  --endpoint-type {{target.api_endpoint.type}} --artifact-dir {{config.output_dir}}
  {% if target.api_endpoint.stream %}--streaming {% endif %}{% if config.params.extra.warmup
  %}--warmup{% endif %}
framework_name: genai_perf_eval
pkg_name: genai_perf_eval
config:
  output_dir: /workspace/evaluation_results
  params:
    limit_samples: null
    max_new_tokens: null
    max_retries: null
    parallelism: 1
    task: genai_perf_generation
    temperature: null
    request_timeout: null
    top_p: null
    extra:
      tokenizer: meta-llama/Llama-3.1-70B-Instruct
      warmup: true
      isl: 500
      osl: 5000
  supported_endpoint_types:
  - chat
  - completions
  type: genai_perf_generation
target:
  api_endpoint:
    api_key_name: null
    model_id: my_model
    stream: null
    type: chat
    url: http://localhost:8000/v1/chat/completions


Rendered command:

genai_perf_eval --model_id my_model --url http://localhost:8000/v1/chat/completions   --concurrencies 1 --isl 500 --osl 5000  --tokenizer meta-llama/Llama-3.1-70B-Instruct --endpoint-type chat --artifact-dir /workspace/evaluation_results --warmup
```

## Customizing your evaluation

## Running GenAI-Perf tasks with different values of ISL and OSL parameters

By default, `nemo-evaluator` supports two tasks: `genai_perf_summarization` and `genai_perf_generation`. The `genai_perf_summarization` task uses an input sequence length (`isl`) of 5000 tokens and an output sequence length (`osl`) of 500 tokens, testing how your endpoint handles tasks with long input and shorter output (e.g. summarizing a long text). The `genai_perf_generation` task uses an `isl` of 500 tokens and an `osl` of 5000 tokens, testing how your endpoint handles tasks with short input and long output (e.g. generating a long text based on a short prompt).

You can run an evaluation with your own set of parameters as follows:

1. Using the command-line tool:

    ```bash
    nemo-evaluator run_eval \
        --eval_type genai_perf_generation \
        --model_id my_model \
        --model_type chat \
        --model_url http://localhost:8000/v1/chat/completions \
        --api_key_name MY_API_KEY \
        --output_dir /workspace/evaluation_results \
        --overrides="config.params.extra.tokenizer=meta-llama/Llama-3.1-70B-Instruct,config.params.extra.isl=<your_isl_value>,config.params.extra.osl=<your_osl_value>"
    ```

2. Using a config file:

    ```bash
    config:
      type: genai_perf_generation
      params:
        parallelism: 1
        extra:
          isl: <your_isl_value>
          osl: <your_osl_value>
          tokenizer: meta-llama/Llama-3.1-70B-Instruct
    target:
      api_endpoint:
        url: http://localhost:8000/v1/chat/completions
        model_id: my_model
        type: chat
    ```

where `<your_isl_value>` and `<your_osl_value>` are your selected values of `isl` and `osl` parameters, respectively.
This allows you to test other common use-cases, e.g. for testing text generation speed you can set `isl=500` and `osl=5000`.

## Testing your endpoint with concurrent requests

By default, `nemo-evaluator` sends a single request at a time to your endpoint.
However, you can test how your endpoint handles traffic by setting the `parallelism` parameter to a higher value, e.g.:

1. Using the command-line tool:

    ```bash
    nemo-evaluator run_eval \
        --eval_type genai_perf_generation \
        --model_id my_model \
        --model_type chat \
        --model_url http://localhost:8000/v1/chat/completions \
        --api_key_name MY_API_KEY \
        --output_dir /workspace/evaluation_results \
        --overrides="config.params.extra.tokenizer=meta-llama/Llama-3.1-70B-Instruct,config.params.parallelism=<num_requests>"
    ```

2. Using a config file:

    ```bash
    config:
      type: genai_perf_generation
      params:
        parallelism: <num_requests>
        extra:
          tokenizer: meta-llama/Llama-3.1-70B-Instruct
    target:
      api_endpoint:
        url: http://localhost:8000/v1/chat/completions
        model_id: my_model
        type: chat
    ```
where `<num_requests>` is the number of concurrent requests send to the model.

# FAQ

## Deploying a model as an endpoint

NVIDIA NeMo Evaluator utilize a client-server communication architecture to interact with the model. As a prerequisite, the **model must be deployed as an endpoint with a NIM-compatible API**.

Users have the flexibility to deploy their model using their own infrastructure and tooling.

Servers with APIs that conform to the OpenAI/NIM API standard are expected to work seamlessly out of the box.


